Recognition of Underlying Causes of Power Quality Disturbances Using Stockwell Transform

Abstract

This paper presents an effective and intelligent methodology for the recognition of several underlying causes responsible for power quality (PQ) disturbances. The disturbance signals at a 40-dB signal-to-noise ratio (SNR) from various locations of the power distribution system have been analyzed with computationally efficient Stockwell transform (ST). Besides the residual voltage and the duration, characteristics like automatic segmented phase angle jump (PAJ) have been extracted and proposed for linking the PQ disturbance with the respective underlying cause. PQ disturbances have been simulated in a MATLAB environment as per the IEEE-1159 standard with five causes, namely symmetrical fault, unsymmetrical fault, induction motor starting, capacitor bank energizing, and transformer energizing. A block scheme has been devised and presented here for the effective recognition of the underlying cause in a power distribution network so that corrective measures can be proposed for the trouble-free operation of the equipment. The performance of the proposed method has also been evaluated with the real-time PQ data to judge the effectiveness of the algorithm in the real environment.